OpenRaiser/Intern-Atlas huggingface dataset:
Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria
Venue: arXiv cs.SI 2020
Year: 2020

Adversarial attacks have been alerting the artificial intelligence community recently, since many machine learning algorithms were found vulnerable to malicious attacks. This paper studies adversarial attacks to scale-free networks to test their robustness in terms of statistical measures. In addition to the well-known random link rewiring (RLR) attack, two heuristic attacks are formulated and simulated: degree-addition-based link rewiring (DALR) and degree-interval-based link rewiring (DILR). These three strategies are applied to attack a number of strong scale-free networks of various sizes generated from the Barabási-Albert model. It is found that both DALR and DILR are more effective than RLR, in the sense that rewiring a smaller number of links can succeed in the same attack. However, DILR is as concealed as RLR in the sense that they both are constructed by introducing a relatively small number of changes on several typical structural properties such as average shortest path-length, average clustering coefficient, and average diagonal distance. The results of this paper suggest that to classify a network to be scale-free has to be very careful from the viewpoint of adversarial attack effects.
Revealing Much While Saying Less: Predictive Wireless for Status Update
Venue: arXiv cs.IT 2020
Year: 2020

Wireless communications for status update are becoming increasingly important, especially for machine-type control applications. Existing work has been mainly focused on Age of Information (AoI) optimizations. In this paper, a status-aware predictive wireless interface design, networking and implementation are presented which aim to minimize the status recovery error of a wireless networked system by leveraging online status model predictions. Two critical issues of predictive status update are addressed: practicality and usefulness. Link-level experiments on a Software-Defined-Radio (SDR) testbed are conducted and test results show that the proposed design can significantly reduce the number of wireless transmissions while maintaining a low status recovery error. A Status-aware Multi-Agent Reinforcement learning neTworking solution (SMART) is proposed to dynamically and autonomously control the transmit decisions of devices in an ad hoc network based on their individual statuses. System-level simulations of a multi dense platooning scenario are carried out on a road traffic simulator. Results show that the proposed schemes can greatly improve the platooning control performance in terms of the minimum safe distance between successive vehicles, in comparison with the AoI-optimized status-unaware and communication latency-optimized schemes---this demonstrates the usefulness of our proposed status update schemes in a real-world application.
Code Duplication on Stack Overflow
Venue: arXiv cs.SE 2020
Year: 2020

Despite the unarguable importance of Stack Overflow (SO) for the daily work of many software developers and despite existing knowledge about the impact of code duplication on software maintainability, the prevalence and implications of code clones on SO have not yet received the attention they deserve. In this paper, we motivate why studies on code duplication within SO are needed and how existing studies on code reuse differ from this new research direction. We present similarities and differences between code clones in general and code clones on SO and point to open questions that need to be addressed to be able to make data-informed decisions about how to properly handle clones on this important platform. We present results from a first preliminary investigation, indicating that clones on SO are common and diverse. We further point to specific challenges, including incentives for users to clone successful answers and difficulties with bulk edits on the platform, and conclude with possible directions for future work.
Public-Key Based Authentication Architecture for IoT Devices Using PUF
Venue: arXiv cs.CR 2020
Year: 2020

Nowadays, Internet of Things (IoT) is a trending topic in the computing world. Notably, IoT devices have strict design requirements and are often referred to as constrained devices. Therefore, security techniques and primitives that are lightweight are more suitable for such devices, e.g., Static Random-Access Memory (SRAM) Physical Unclonable Functions (PUFs) and Elliptic Curve Cryptography (ECC). SRAM PUF is an intrinsic security primitive that is seeing widespread adoption in the IoT segment. ECC is a public-key algorithm technique that has been gaining popularity among constrained IoT devices. The popularity is due to using significantly smaller operands when compared to other public-key techniques such as RSA (Rivest Shamir Adleman). This paper shows the design, development, and evaluation of an application-specific secure communication architecture based on SRAM PUF technology and ECC for constrained IoT devices. More specifically, it introduces an Elliptic Curve Diffie-Hellman (ECDH) public-key based cryptographic protocol that utilizes PUF-derived keys as the root-of-trust for silicon authentication. Also, it proposes a design of a modular hardware architecture that supports the protocol. Finally, to analyze the practicality as well as the feasibility of the proposed protocol, we demonstrate the solution by prototyping and verifying a protocol variant on the commercial Xilinx Zynq-7000 APSoC device.
Extracting Dense and Connected Subgraphs in Dual Networks by Network Alignment
Venue: arXiv cs.DS 2020
Year: 2020

The use of network based approaches to model and analyse large datasets is currently a growing research field. For instance in biology and medicine, networks are used to model interactions among biological molecules as well as relations among patients. Similarly, data coming from social networks can be trivially modelled by using graphs. More recently, the use of dual networks gained the attention of researchers. A dual network model uses a pair of graphs to model a scenario in which one of the two graphs is usually unweighted (a network representing physical associations among nodes) while the other one is edge-weighted (a network representing conceptual associations among nodes). In this paper we focus on the problem of finding the Densest Connected sub-graph (DCS) having the largest density in the conceptual network which is also connected in the physical network. The problem is relevant but also computationally hard, therefore the need for introducing of novel algorithms arises. We formalise the problem and then we map DCS into a graph alignment problem. Then we propose a possible solution. A set of experiments is also presented to support our approach.
Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings
Venue: arXiv cs.CR 2020
Year: 2020

Twitter is a web application playing dual roles of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. Legitimate bots generate a large amount of benign contextual content, i.e., tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human and spambot accounts on Twitter, by employing recurrent neural networks, specifically bidirectional Long Short-term Memory (BiLSTM), to efficiently capture features across tweets. To the best of our knowledge, our work is the first that develops a recurrent neural model with word embeddings to distinguish Twitter bots from human accounts, that requires no prior knowledge or assumption about users' profiles, friendship networks, or historical behavior on the target account. Moreover, our model does not require any handcrafted features. The preliminary simulation results are very encouraging. Experiments on the cresci-2017 dataset show that our approach can achieve competitive performance compared with existing state-of-the-art bot detection systems.
Visual Concept-Metaconcept Learning
Venue: arXiv cs.CV 2020
Year: 2020

Humans reason with concepts and metaconcepts: we recognize red and green from visual input; we also understand that they describe the same property of objects (i.e., the color). In this paper, we propose the visual concept-metaconcept learner (VCML) for joint learning of concepts and metaconcepts from images and associated question-answer pairs. The key is to exploit the bidirectional connection between visual concepts and metaconcepts. Visual representations provide grounding cues for predicting relations between unseen pairs of concepts. Knowing that red and green describe the same property of objects, we generalize to the fact that cube and sphere also describe the same property of objects, since they both categorize the shape of objects. Meanwhile, knowledge about metaconcepts empowers visual concept learning from limited, noisy, and even biased data. From just a few examples of purple cubes we can understand a new color purple, which resembles the hue of the cubes instead of the shape of them. Evaluation on both synthetic and real-world datasets validates our claims.
Lightweight Convolutional Representations for On-Device Natural Language Processing
Venue: arXiv cs.CL 2020
Year: 2020

The increasing computational and memory complexities of deep neural networks have made it difficult to deploy them on low-resource electronic devices (e.g., mobile phones, tablets, wearables). Practitioners have developed numerous model compression methods to address these concerns, but few have condensed input representations themselves. In this work, we propose a fast, accurate, and lightweight convolutional representation that can be swapped into any neural model and compressed significantly (up to 32x) with a negligible reduction in performance. In addition, we show gains over recurrent representations when considering resource-centric metrics (e.g., model file size, latency, memory usage) on a Samsung Galaxy S9.
Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support
Venue: arXiv cs.HC 2020
Year: 2020

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms' complexity is a reason for their increased performance, it also leads to the "black box" problem, consequently decreasing trust towards AI. In this regard, "Explainable Artificial Intelligence" (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability.
StreamFlow: cross-breeding cloud with HPC
Venue: arXiv cs.DC 2020
Year: 2020

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single-cell transcriptomic data analysis workflow.
Whose Side are Ethics Codes On? Power, Responsibility and the Social Good
Venue: arXiv cs.CY 2020
Year: 2020

The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker's hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the "social good", of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes.
Simplifying Activity-on-Edge Graphs
Venue: arXiv cs.DS 2020
Year: 2020

We formalize the simplification of activity-on-edge graphs used for visualizing project schedules, where the vertices of the graphs represent project milestones, and the edges represent either tasks of the project or timing constraints between milestones. In this framework, a timeline of the project can be constructed as a leveled drawing of the graph, where the levels of the vertices represent the time at which each milestone is scheduled to happen. We focus on the following problem: given an activity-on-edge graph representing a project, find an equivalent activity-on-edge graph (one with the same critical paths) that has the minimum possible number of milestone vertices among all equivalent activity-on-edge graphs. We provide a polynomial-time algorithm for solving this graph minimization problem.
Demystifying content-blockers: A large scale study of actual performance gains
Venue: arXiv cs.NI 2020
Year: 2020

With the evolution of the online advertisement and tracking ecosystem, content-filtering has become the reference tool for improving the security, privacy and browsing experience when surfing the Internet. It is also commonly believed that using content-blockers to stop unsolicited content decreases the time needed for loading websites. In this work, we perform a large scale study with the 100K most popular websites on the actual performance improvements of using content-blockers. We focus our study in the two most relevant metrics for user experience; bandwidth and latency. Our results show that using such tools results in small improvements in terms of bandwidth usage but, contrary to popular belief, it has a negligible impact in terms of loading time. We also find that, in the case of small and fast loading websites, the use of content-blockers can even result in increased browsing latency.
Experiments with Different Indexing Techniques for Text Retrieval tasks on Gujarati Language using Bag of Words Approach
Venue: arXiv cs.IR 2020
Year: 2020

This paper presents results of various experiments carried out to improve text retrieval of gujarati text documents. Text retrieval involves searching and ranking of text documents for a given set of query terms. We have tested various retrieval models that uses bag-of-words approach. Bag-of-words approach is a traditional approach that is being used till date where the text document is represented as collection of words. Measures like frequency count, inverse document frequency etc. are used to signify and rank relevant documents for user queries. Different ranking models have been used to quantify ranking performance using the metric of mean average precision. Gujarati is a morphologically rich language, we have compared techniques like stop word removal, stemming and frequent case generation against baseline to measure the improvements in information retrieval tasks. Most of the techniques are language dependent and requires development of language specific tools. We used plain unprocessed word index as the baseline, we have seen significant improvements in comparison of MAP values after applying different indexing techniques when compared to the baseline.
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study
Venue: arXiv cs.LG 2020
Year: 2020

Despite achieving remarkable performance on many image classification tasks, state-of-the-art machine learning (ML) classifiers remain vulnerable to small input perturbations. Especially, the existence of adversarial examples raises concerns about the deployment of ML models in safety- and security-critical environments, like autonomous driving and disease detection. Over the last few years, numerous defense methods have been published with the goal of improving adversarial as well as corruption robustness. However, the proposed measures succeeded only to a very limited extent. This limited progress is partly due to the lack of understanding of the decision boundary and decision regions of deep neural networks. Therefore, we study the minimum distance of data points to the decision boundary and how this margin evolves over the training of a deep neural network. By conducting experiments on MNIST, FASHION-MNIST, and CIFAR-10, we observe that the decision boundary moves closer to natural images over training. This phenomenon even remains intact in the late epochs of training, where the classifier already obtains low training and test error rates. On the other hand, adversarial training appears to have the potential to prevent this undesired convergence of the decision boundary.
Geosocial Location Classification: Associating Type to Places Based on Geotagged Social-Media Posts
Venue: arXiv cs.CL 2020
Year: 2020

Associating type to locations can be used to enrich maps and can serve a plethora of geospatial applications. An automatic method to do so could make the process less expensive in terms of human labor, and faster to react to changes. In this paper we study the problem of Geosocial Location Classification, where the type of a site, e.g., a building, is discovered based on social-media posts. Our goal is to correctly associate a set of messages posted in a small radius around a given location with the corresponding location type, e.g., school, church, restaurant or museum. We explore two approaches to the problem: (a) a pipeline approach, where each message is first classified, and then the location associated with the message set is inferred from the individual message labels; and (b) a joint approach where the individual messages are simultaneously processed to yield the desired location type. We tested the two approaches over a dataset of geotagged tweets. Our results demonstrate the superiority of the joint approach. Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, linear classifiers outperform deep neural network alternatives.
Zendoo: a zk-SNARK Verifiable Cross-Chain Transfer Protocol Enabling Decoupled and Decentralized Sidechains
Venue: arXiv cs.CR 2020
Year: 2020

Sidechains are an appealing innovation devised to enable blockchain scalability and extensibility. The basic idea is simple yet powerful: construct a parallel chain -- sidechain -- with desired features, and provide a way to transfer coins between the mainchain and the sidechain. In this paper, we introduce Zendoo, a construction for Bitcoin-like blockchain systems that allows the creation and communication with sidechains of different types without knowing their internal structure. We consider a parent-child relationship between the mainchain and sidechains, where sidechain nodes directly observe the mainchain while mainchain nodes only observe cryptographically authenticated certificates from sidechain maintainers. We use zk-SNARKs to construct a universal verifiable transfer mechanism that is used by sidechains. Moreover, we propose a specific sidechain construction, named Latus, that can be built on top of this infrastructure, and realizes a decentralized verifiable blockchain system for payments. We leverage the use of recursive composition of zk-SNARKs to generate succinct proofs of sidechain state progression that are used to generate certificates' validity proofs. This allows the mainchain to efficiently verify all operations performed in the sidechain without knowing any details about those operations.
Weight distributions of several families of 3-weight binary linear codes
Venue: arXiv cs.IT 2020
Year: 2020

The linear codes with a few weights have been applied widely in combinatorial designs, secret sharing, association schemes, authentication codes and strongly regular graphs. In this paper, we first correct an erroneous result about the exponential sum $\sum_{x\in \mathbb{F}_{2^{e}}}χ_1\left(ax^{2^α+1}+bx\right).$ Then, using the above exponential sum, we construct several families of binary linear codes of $3$-weight and determine their weight distributions. Moreover, Most of them can be used in secret sharing schemes.
Generation of smoothly-varying infill configurations from a continuous menu of cell patterns and the asymptotic analysis of its mechanical behaviour
Venue: arXiv cs.CE 2020
Year: 2020

We here introduce a novel scheme for generating smoothly-varying infill graded microstructural (IGM) configurations from a given menu of generating cells. The scheme was originally proposed for essentially improving the variety of describable configurations in a modified asymptotic homogenisation-based topology optimisation framework [1] for fast IGM design. But the proposed scheme, after modification, also demonstrates its unique values in two aspects of applications. First, it provides a fairly simple way of generating an IGM configuration continuously patching any given cell configurations. Second, it tenders a straightforward mean for decorating microstructures on a given manifold. We will further show that the form of topology description function given here effectively offers a platform for unifying most existing approaches for IGM generation. Fuelled by asymptotic analysis of the mechanical behaviour of the resulting IGM configurations, a topology optimisation scheme for compliance minimisation is introduced. We will finally show that, the use of the present scheme helps reduce the compliance value of an optimised structure by nearly a half, if compared with that from the original framework [1].